The progression of disorder-specific brain pattern expression in schizophrenia over 9 years

Abstract Age plays a crucial role in the performance of schizophrenia vs. controls (SZ-HC) neuroimaging-based machine learning (ML) models as the accuracy of identifying first-episode psychosis from controls is poor compared to chronic patients. Resolving whether this finding reflects longitudinal p...

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Autores principales:	Johannes Lieslehto, Erika Jääskeläinen, Vesa Kiviniemi, Marianne Haapea, Peter B. Jones, Graham K. Murray, Juha Veijola, Udo Dannlowski, Dominik Grotegerd, Susanne Meinert, Tim Hahn, Anne Ruef, Matti Isohanni, Peter Falkai, Jouko Miettunen, Dominic B. Dwyer, Nikolaos Koutsouleris
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Psychiatry RC435-571
Acceso en línea:	https://doaj.org/article/0450943ac8214069a14c3083b21b944e
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spelling	oai:doaj.org-article:0450943ac8214069a14c3083b21b944e2021-12-02T17:40:42ZThe progression of disorder-specific brain pattern expression in schizophrenia over 9 years10.1038/s41537-021-00157-02334-265Xhttps://doaj.org/article/0450943ac8214069a14c3083b21b944e2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41537-021-00157-0https://doaj.org/toc/2334-265XAbstract Age plays a crucial role in the performance of schizophrenia vs. controls (SZ-HC) neuroimaging-based machine learning (ML) models as the accuracy of identifying first-episode psychosis from controls is poor compared to chronic patients. Resolving whether this finding reflects longitudinal progression in a disorder-specific brain pattern or a systematic but non-disorder-specific deviation from a normal brain aging (BA) trajectory in schizophrenia would help the clinical translation of diagnostic ML models. We trained two ML models on structural MRI data: an SZ-HC model based on 70 schizophrenia patients and 74 controls and a BA model (based on 561 healthy individuals, age range = 66 years). We then investigated the two models’ predictions in the naturalistic longitudinal Northern Finland Birth Cohort 1966 (NFBC1966) following 29 schizophrenia and 61 controls for nine years. The SZ-HC model’s schizophrenia-specificity was further assessed by utilizing independent validation (62 schizophrenia, 95 controls) and depression samples (203 depression, 203 controls). We found better performance at the NFBC1966 follow-up (sensitivity = 75.9%, specificity = 83.6%) compared to the baseline (sensitivity = 58.6%, specificity = 86.9%). This finding resulted from progression in disorder-specific pattern expression in schizophrenia and was not explained by concomitant acceleration of brain aging. The disorder-specific pattern’s progression reflected longitudinal changes in cognition, outcomes, and local brain changes, while BA captured treatment-related and global brain alterations. The SZ-HC model was also generalizable to independent schizophrenia validation samples but classified depression as control subjects. Our research underlines the importance of taking account of longitudinal progression in a disorder-specific pattern in schizophrenia when developing ML classifiers for different age groups.Johannes LieslehtoErika JääskeläinenVesa KiviniemiMarianne HaapeaPeter B. JonesGraham K. MurrayJuha VeijolaUdo DannlowskiDominik GrotegerdSusanne MeinertTim HahnAnne RuefMatti IsohanniPeter FalkaiJouko MiettunenDominic B. DwyerNikolaos KoutsoulerisNature PortfolioarticlePsychiatryRC435-571ENnpj Schizophrenia, Vol 7, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Psychiatry RC435-571
spellingShingle	Psychiatry RC435-571 Johannes Lieslehto Erika Jääskeläinen Vesa Kiviniemi Marianne Haapea Peter B. Jones Graham K. Murray Juha Veijola Udo Dannlowski Dominik Grotegerd Susanne Meinert Tim Hahn Anne Ruef Matti Isohanni Peter Falkai Jouko Miettunen Dominic B. Dwyer Nikolaos Koutsouleris The progression of disorder-specific brain pattern expression in schizophrenia over 9 years
description	Abstract Age plays a crucial role in the performance of schizophrenia vs. controls (SZ-HC) neuroimaging-based machine learning (ML) models as the accuracy of identifying first-episode psychosis from controls is poor compared to chronic patients. Resolving whether this finding reflects longitudinal progression in a disorder-specific brain pattern or a systematic but non-disorder-specific deviation from a normal brain aging (BA) trajectory in schizophrenia would help the clinical translation of diagnostic ML models. We trained two ML models on structural MRI data: an SZ-HC model based on 70 schizophrenia patients and 74 controls and a BA model (based on 561 healthy individuals, age range = 66 years). We then investigated the two models’ predictions in the naturalistic longitudinal Northern Finland Birth Cohort 1966 (NFBC1966) following 29 schizophrenia and 61 controls for nine years. The SZ-HC model’s schizophrenia-specificity was further assessed by utilizing independent validation (62 schizophrenia, 95 controls) and depression samples (203 depression, 203 controls). We found better performance at the NFBC1966 follow-up (sensitivity = 75.9%, specificity = 83.6%) compared to the baseline (sensitivity = 58.6%, specificity = 86.9%). This finding resulted from progression in disorder-specific pattern expression in schizophrenia and was not explained by concomitant acceleration of brain aging. The disorder-specific pattern’s progression reflected longitudinal changes in cognition, outcomes, and local brain changes, while BA captured treatment-related and global brain alterations. The SZ-HC model was also generalizable to independent schizophrenia validation samples but classified depression as control subjects. Our research underlines the importance of taking account of longitudinal progression in a disorder-specific pattern in schizophrenia when developing ML classifiers for different age groups.
format	article
author	Johannes Lieslehto Erika Jääskeläinen Vesa Kiviniemi Marianne Haapea Peter B. Jones Graham K. Murray Juha Veijola Udo Dannlowski Dominik Grotegerd Susanne Meinert Tim Hahn Anne Ruef Matti Isohanni Peter Falkai Jouko Miettunen Dominic B. Dwyer Nikolaos Koutsouleris
author_facet	Johannes Lieslehto Erika Jääskeläinen Vesa Kiviniemi Marianne Haapea Peter B. Jones Graham K. Murray Juha Veijola Udo Dannlowski Dominik Grotegerd Susanne Meinert Tim Hahn Anne Ruef Matti Isohanni Peter Falkai Jouko Miettunen Dominic B. Dwyer Nikolaos Koutsouleris
author_sort	Johannes Lieslehto
title	The progression of disorder-specific brain pattern expression in schizophrenia over 9 years
title_short	The progression of disorder-specific brain pattern expression in schizophrenia over 9 years
title_full	The progression of disorder-specific brain pattern expression in schizophrenia over 9 years
title_fullStr	The progression of disorder-specific brain pattern expression in schizophrenia over 9 years
title_full_unstemmed	The progression of disorder-specific brain pattern expression in schizophrenia over 9 years
title_sort	progression of disorder-specific brain pattern expression in schizophrenia over 9 years
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0450943ac8214069a14c3083b21b944e
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The progression of disorder-specific brain pattern expression in schizophrenia over 9 years

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